{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第8章 DQN改进算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import gym\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import rl_utils\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "class Qnet(torch.nn.Module):\n",
    "    ''' 只有一层隐藏层的Q网络 '''\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim):\n",
    "        super(Qnet, self).__init__()\n",
    "        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)\n",
    "        self.fc2 = torch.nn.Linear(hidden_dim, action_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        return self.fc2(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DQN:\n",
    "    ''' DQN算法,包括Double DQN '''\n",
    "    def __init__(self,\n",
    "                 state_dim,\n",
    "                 hidden_dim,\n",
    "                 action_dim,\n",
    "                 learning_rate,\n",
    "                 gamma,\n",
    "                 epsilon,\n",
    "                 target_update,\n",
    "                 device,\n",
    "                 dqn_type='VanillaDQN'):\n",
    "        self.action_dim = action_dim\n",
    "        self.q_net = Qnet(state_dim, hidden_dim, self.action_dim).to(device)\n",
    "        self.target_q_net = Qnet(state_dim, hidden_dim,\n",
    "                                 self.action_dim).to(device)\n",
    "        self.optimizer = torch.optim.Adam(self.q_net.parameters(),\n",
    "                                          lr=learning_rate)\n",
    "        self.gamma = gamma\n",
    "        self.epsilon = epsilon\n",
    "        self.target_update = target_update\n",
    "        self.count = 0\n",
    "        self.dqn_type = dqn_type\n",
    "        self.device = device\n",
    "\n",
    "    def take_action(self, state):\n",
    "        if np.random.random() < self.epsilon:\n",
    "            action = np.random.randint(self.action_dim)\n",
    "        else:\n",
    "            state = torch.tensor([state], dtype=torch.float).to(self.device)\n",
    "            action = self.q_net(state).argmax().item()\n",
    "        return action\n",
    "\n",
    "    def max_q_value(self, state):\n",
    "        state = torch.tensor([state], dtype=torch.float).to(self.device)\n",
    "        return self.q_net(state).max().item()\n",
    "\n",
    "    def update(self, transition_dict):\n",
    "        states = torch.tensor(transition_dict['states'],\n",
    "                              dtype=torch.float).to(self.device)\n",
    "        actions = torch.tensor(transition_dict['actions']).view(-1, 1).to(\n",
    "            self.device)\n",
    "        rewards = torch.tensor(transition_dict['rewards'],\n",
    "                               dtype=torch.float).view(-1, 1).to(self.device)\n",
    "        next_states = torch.tensor(transition_dict['next_states'],\n",
    "                                   dtype=torch.float).to(self.device)\n",
    "        dones = torch.tensor(transition_dict['dones'],\n",
    "                             dtype=torch.float).view(-1, 1).to(self.device)\n",
    "\n",
    "        q_values = self.q_net(states).gather(1, actions)  # Q值\n",
    "        # 下个状态的最大Q值\n",
    "        if self.dqn_type == 'DoubleDQN': # DQN与Double DQN的区别\n",
    "            max_action = self.q_net(next_states).max(1)[1].view(-1, 1) \n",
    "            max_next_q_values = self.target_q_net(next_states).gather(1, max_action)\n",
    "        else: # DQN的情况\n",
    "            max_next_q_values = self.target_q_net(next_states).max(1)[0].view(-1, 1)\n",
    "        q_targets = rewards + self.gamma * max_next_q_values * (1 - dones)  # TD误差目标\n",
    "        dqn_loss = torch.mean(F.mse_loss(q_values, q_targets))  # 均方误差损失函数\n",
    "        self.optimizer.zero_grad()  # PyTorch中默认梯度会累积,这里需要显式将梯度置为0\n",
    "        dqn_loss.backward()  # 反向传播更新参数\n",
    "        self.optimizer.step()\n",
    "\n",
    "        if self.count % self.target_update == 0:\n",
    "            self.target_q_net.load_state_dict(\n",
    "                self.q_net.state_dict())  # 更新目标网络\n",
    "        self.count += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 1e-2\n",
    "num_episodes = 200\n",
    "hidden_dim = 128\n",
    "gamma = 0.98\n",
    "epsilon = 0.01\n",
    "target_update = 50\n",
    "buffer_size = 5000\n",
    "minimal_size = 1000\n",
    "batch_size = 64\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\n",
    "    \"cpu\")\n",
    "\n",
    "env_name = 'Pendulum-v0'\n",
    "env = gym.make(env_name)\n",
    "state_dim = env.observation_space.shape[0]\n",
    "action_dim = 11  # 将连续动作分成11个离散动作\n",
    "\n",
    "\n",
    "def dis_to_con(discrete_action, env, action_dim):  # 离散动作转回连续的函数\n",
    "    action_lowbound = env.action_space.low[0]  # 连续动作的最小值\n",
    "    action_upbound = env.action_space.high[0]  # 连续动作的最大值\n",
    "    return action_lowbound + (discrete_action /\n",
    "                              (action_dim - 1)) * (action_upbound -\n",
    "                                                   action_lowbound)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_DQN(agent, env, num_episodes, replay_buffer, minimal_size,\n",
    "              batch_size):\n",
    "    return_list = []\n",
    "    max_q_value_list = []\n",
    "    max_q_value = 0\n",
    "    for i in range(10):\n",
    "        with tqdm(total=int(num_episodes / 10),\n",
    "                  desc='Iteration %d' % i) as pbar:\n",
    "            for i_episode in range(int(num_episodes / 10)):\n",
    "                episode_return = 0\n",
    "                state = env.reset()\n",
    "                done = False\n",
    "                while not done:\n",
    "                    action = agent.take_action(state)\n",
    "                    max_q_value = agent.max_q_value(\n",
    "                        state) * 0.005 + max_q_value * 0.995  # 平滑处理\n",
    "                    max_q_value_list.append(max_q_value)  # 保存每个状态的最大Q值\n",
    "                    action_continuous = dis_to_con(action, env,\n",
    "                                                   agent.action_dim)\n",
    "                    next_state, reward, done, _ = env.step([action_continuous])\n",
    "                    replay_buffer.add(state, action, reward, next_state, done)\n",
    "                    state = next_state\n",
    "                    episode_return += reward\n",
    "                    if replay_buffer.size() > minimal_size:\n",
    "                        b_s, b_a, b_r, b_ns, b_d = replay_buffer.sample(\n",
    "                            batch_size)\n",
    "                        transition_dict = {\n",
    "                            'states': b_s,\n",
    "                            'actions': b_a,\n",
    "                            'next_states': b_ns,\n",
    "                            'rewards': b_r,\n",
    "                            'dones': b_d\n",
    "                        }\n",
    "                        agent.update(transition_dict)\n",
    "                return_list.append(episode_return)\n",
    "                if (i_episode + 1) % 10 == 0:\n",
    "                    pbar.set_postfix({\n",
    "                        'episode':\n",
    "                        '%d' % (num_episodes / 10 * i + i_episode + 1),\n",
    "                        'return':\n",
    "                        '%.3f' % np.mean(return_list[-10:])\n",
    "                    })\n",
    "                pbar.update(1)\n",
    "    return return_list, max_q_value_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 0:   0%|          | 0/20 [00:00<?, ?it/s]<ipython-input-2-06a52ba6b3ad>:30: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  /Users/distiller/project/pytorch/torch/csrc/utils/tensor_new.cpp:210.)\n",
      "  state = torch.tensor([state], dtype=torch.float).to(self.device)\n",
      "Iteration 0: 100%|██████████| 20/20 [00:02<00:00,  7.14it/s, episode=20, return=-1018.764]\n",
      "Iteration 1: 100%|██████████| 20/20 [00:03<00:00,  5.73it/s, episode=40, return=-463.311]\n",
      "Iteration 2: 100%|██████████| 20/20 [00:03<00:00,  5.53it/s, episode=60, return=-184.817]\n",
      "Iteration 3: 100%|██████████| 20/20 [00:03<00:00,  5.55it/s, episode=80, return=-317.366]\n",
      "Iteration 4: 100%|██████████| 20/20 [00:03<00:00,  5.67it/s, episode=100, return=-208.929]\n",
      "Iteration 5: 100%|██████████| 20/20 [00:03<00:00,  5.59it/s, episode=120, return=-182.659]\n",
      "Iteration 6: 100%|██████████| 20/20 [00:03<00:00,  5.25it/s, episode=140, return=-275.938]\n",
      "Iteration 7: 100%|██████████| 20/20 [00:03<00:00,  5.65it/s, episode=160, return=-209.702]\n",
      "Iteration 8: 100%|██████████| 20/20 [00:03<00:00,  5.73it/s, episode=180, return=-246.861]\n",
      "Iteration 9: 100%|██████████| 20/20 [00:03<00:00,  5.77it/s, episode=200, return=-293.374]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HXjcfXj6DPa6fFXt/wuRB3H7KJM82f3lrKdc//0Xs/aDuBZTk5/DlunJG9y1m1i8PBODCxz7m6flruPnE3ThljyEAlFXVs7WqjuG9ixLK6MaR8e1fH8zf3l3Gg+8u529nTeWHD1nfwTMX7OeJ0xx/17t8snKbZx+3njyRk6YMjr2/9D+f8pidyACw5PojGX25Zek68ZitlXXs/rtXPPv5zp5DuPGE3QDrvpm/chtTh/eMrb9o5seMH1DKOQeOSuncUmHJhnJm3P4WAD/ebwQnThnMkX94m2G9Cjll6hDOO2hUsy7P9kRE5hljpgatU0tDyXrueOUrxlz+QqDP3MEJbPcpyQOIq0Qb5K6fMKCU3Jz4n1CvolxCCX7g9585xd6fYbsrY6o0PxK3bZDCACtdd3t1Q+A6h+YeBut8ExadzW9/eVHM5+9WGAD9u+XzjW2JueeGrNhsuVLCrjjK9Nvf5KBb30gqQyK5nDTnj1Zsiy33z2tZ6nP9gdfS+Os7yzwKA4gpDDdfriuPW+aUgwG4/KlPOene2TEL1BjDfz9Zw40vxCvsHz30IT/+e+seUp+YuyoWh2qIGh5810rPXrG5ilteWsSmisxJJ1aloWQ9zuBbWesdaMuq67nr9SVEo4YN5Y4bxvrhbiz3umWCBuE+pXmBx+teGPEMoG4caydqoNLleigtaD68eIFtOUSNoTxBldxnf7ZfbP/JuPv1JZ73UWOIRg1/fG0JF/zzYzb53FJgXRPHXdIQjWKMYWtlHfNXlcX24RD0+UQ0uBTYuY/Mi30Xi1wuNb/S2F4TrzQbXCf9u2c/T+nYSzcFKB/XsZ6ev8azLFlZ+1e/3MCsL9Y3e8zahkZOuW82H32zlcao4dxH5nH/W0vZZ3RvehblUtsQ5ZXPvfuJGquk/jXPfJb2yZyqNJSsxp2W6v+xXfLvBdzy0iI+XL6FrbZ/3xjDJyu38afX4gdVP32Kg5XG1OE9E/rFneV+WfyWRlDpkl7FubaMJJzX4S9f4t/PqD6Wu8iptuvQGDWscc09+Wp9/BO4Y2X0Ls5j5ZZqRlz6vMe1k4qnOxqNz5Jyz6z/an3TIO4eOKMJ4jcTBpTywWVWX7bWFIF0zskjo2s3TpwjauC9rzex3+9fB6Ak36vkk1mxbrZV1fHveav5YNkWrnv2c77eWMELdofHaSN7khMSPv5mK1urvN9v1Bh+9NCHPPju8kArqzNRpaFkNYs3NA1+Db6BevEG68dXlJcTG8SjBp6ZH5+S6czgdlscPYrig+DLbzqanj731MM/3DP22rFA/JaLX2kEKYVetpKKGsOWqmCl4fi9nVNd7woY/+aIcZx30OjAzxljpc06nPnXDwK3AxiZIE7hDOzJXGOX/udTxl35omfZd/8SnO7r3XfTa3fA/YYTdrUyv4DGAEWbCGMMSzZUBM6jCZI/agx/dLkLR9pzWl5cuNZK0w1QPkFMv+1NLnvKSv8d07eEL1ypxHsM70kkHAp0mTVGTWx5sthcZ6BKQ8lKvt5Ywd1vLPE8Mfuf7p3Bxz3AR43xuLF+MWNsbDlAuWtdL5fS+OSqQ1lw9WGx945y6FWUy/gBpbHlzrH8svhjI9sClIJzvKiBTeW17DywlJ8GBGND0jTwuX3h/Urz4txmJ00ZzJCeBTRGjUdpJHOBDHXVvnLjPOlvSVDOY+7yLTw+d6V1Dvb+K2qTx2b2GtHTsz00fW///MleTBrSnbBzTRNMegzCGDjizrcC1znf9YhLmxIIolHj+U5K7Gyznz7yET/6+1zPtUvGZte1GdKzwFMRefyAUs9945HJdUqp1i3rKFRpKFnJMX98h5tfXOSpGeV31TgDlvtHGDXGM5CdOGUQ0JTwua3SGjh+dfhO5EeaZml3L8z1WAvO2FySn0MkbL3JzQk1KQ3fD98/EJRVxw+8jnsqagybKmrZZWA3ClwyPPaTafaxJba/za7YQt+S/LhZ1beePJGi3ByixrB0Y/DA98Hl0zl0Qj8ASvNzKHLNTveeg/U/KJW4rKqek+5tmnfjnP+GgNRZN4O6F3i2h6Z02wH2PJSwfX1/9+znnPnXOR6lNXFwN/Yf0ztAVhNneb78iwMY1L2AqLHiLO6vJGq8itz/fTllVQb3sGSqqW+MT4P2ISKeSYvFeTnk2OcyYWBpLCkDvO7CVJRGQwusrpaiSkPJSpwy5A+9tzy2LNHTs7v1qjFNAfP+pfmxQd55ct9ixz7GDyhJenznc6UFkZj7ZI/hPWLKxP+79w8EzgC1z6hesWXOnJAr/ruQDeW1dC+MxD534SGj2dve1lIa1mf+ZmfhlObnMHlY01O5X9a5K7Z6rpXD8xfuT9+S/NjnepfkEQ4FDxvONdpaFa/wNpR7lYPzXWwoTx4wH2QPwu7L4yil/nbZEPc5vb14kycIf8epk9hnVJDS8L4f3KOAsf1KCIWs72Kzz1qKGuM5r6gxnrk8j7xvxYic+S7Tb3uTCVe95NmHPxHjlpcW8e4Sb2FJ50wmDCxl9iWHcNvJEwGrJ0si2YM479GPOPW+2c1v2ApUaSg7DP4nSwd3ppQ7XlBakBMb/J2POhPiuhXExzPcOJ8ryc+hOC+Hx8+exr1nTIm5H/wKzF/11lEaFx+2U2yZ35ddWhCJDUTFrsCsiHUe26rqYoPSnMtmUJibQ5A7PBQKdin1L81ngh1Yd+TuXZQXexr245yTP4gbtKwhRaXRt6QpjuOwrqyG0vycWD0uv8vNPSGxb2l+4Dm793feQaNic05CIhjjjQU551brcntFTXCGmLPboCyrRJM5+5bk8e9z9waa3FcjehWREw4FpnSnkj319cYKuhXEp3G3B6o0lKyjtsGbyXLg2D6A9WPbWlnHQ+8u8zwlepUGlNtB6F0GdotZBs4g48wE92fP+HHmQThPnnuN7EVJfiTmHvLP9I13T1ky9HT5tv1ZvN0KIjFXWnGe2zUmRKPGM5fDGWDd8Zt/n7sPQKD1AVYnQgdnk94luQnTif2K1Y1fKTXaAYjmypk7wX/3QLmlqo7ersw1vzybXXGcotyw55y/NXFg3DEG9yiMuRod196G7V6F4P9+TIA1ErSdm0SuuJ8dMpopw6zYjfOw4FhYQde6OfdUNGpYubU65YmVLUWVhpJ1rC/z/uDH9bdcSQ1Rw4UzP+bqZz7n/WVNbgG/peEMltd/e9e4bCTnyb4wgV/foaImfjCHpkHbP8/CPw5sq65HBHoUNn3ePyO4W0EkFph3WxohsX3wAXER9yA0ZViPwP069AkYmHsX58UVQ/z1EZY1NPvrzfx73iq2VrrdfYaVW6p4cp631lKDbVo5ExzP2md4oAyO0nRfn7KqeroVxsePHBwL4PNrD0dEYud3xrShsZRkt6uoZ5H7GlvXbr3PnebPZosab7yoabn3i3zsg29ix0pkVfUOSN12YjlBk0SbC2lsKK+lriHKkJ7BCQttRZWGknX46xI5P57GaJTl9uxl96Bx31tNaZfGWFkyh03oR0Fu2BWD8Gb7JKrV5DDSng9x8Lg+nuXOU71/clqcpVFVR0leTiweAgksDXs/JS55QiGx3VPxbiJ/IDxovw5H7jog9tp50u9dnBc3kJ061SodMuuL9Vz8r/ke339j1HDwrW/ETXpz9re9pp7C3DAXTh8TKIOjnN2WxrbqOk/NL7/S21ZVT25OKFb6vcr+zkryIzHZ3VllA1yFHR1LY73P0lhrVwz4w2mT2H9M71gygh93TAysFONb7ArF/riOQ++SeKUxsHtiSyPIPbVySxVvfrWRdxZviqWMD+sgpZHdVW6VrOHrjRUs21jJ5f/9lLtPnxJ7SnbTGDXUNUTjlIZj6jc0GsrtQXZzgrIM0ag16Ow6yHr6jFkaUcc95VgayX86U4f35O1fHxzLpnFwxje/pRE047m0IOJxHfkH61K3e8pjaVh++W0Bcz2c/UVccYmggek3R4zz1HFyrlv3wkic68Z/LbZ5AsbBsSQnG2p7dQMl+TnkR5qU46xfHsiM29/0nPOPH57L6XsN5fpv78q2qnrG9k2ciLC9pt6TyVYeUxo5sevvWJcnTRnMxCHdY9s66cr+igAf23WuehXlxdx/QaU9DPGuOGfin18ROew6qFvcsiZ3Wfz2a7ZVM7xXUczaenLeKv7vX/PjtlNLw0ZEjhCRRSKyREQuSbc8Sucw/bY3+fHDc1m/vZa/2X24/fzqyfmMv+pF1vsCjoWRpqdV5+n7t09/FrgPp0RHqR1EjFka9vrKukZyEwQo/QzpWRj3FOwM0H53R6NvXK2sbaA4LwcnUck/YRAsS+PGE3blqF37M3Fw99jykCsQDnCfXe/KfXz3oBrkntqpv7chk6OcuhVE4hSeP5nK3aAqkf+9odGZ92IN8O6quW6F5j5nZxa73z3lZ3t1vacsiyNvqcvS2FhhyXjGtGHecxEhGrVSngd1L+CB71k1+5w6Vk6JGH8gfHivQo6dOJCoMXHfbbfCCDX1jfzVvm9Pdinj206e6EndvvPUSfzfYWOb5HFpjQfP2gOAcx/9iInXvhyLB735VXAXxb4BFkx70KWUhoiEgbuAI4EJwHdEZEJHH7eqroHNFbWsK6th9bZqtlTWpTSBSGkf/Jko7sllLy5cy+X2DNv/fGS1+/RbGk62T32SrJPD7HkIjVFDVX0jRba7JyimUZSXPJ6RDEfZOMrrvjOncODYPnGzkKvqLBlywyEOndCPO06dFDc4l+TnMLZfCXefPsWjxEIiPDx7BVf9z1KMB+/UN7bOURruQL5jffxg3+GxJ35/yXfHwirNj8SsDvfx3LjLgiTK9Im5p6obPGnJbhkhXiHVN0Ypr22ge5LstWcXrPXMOXFcgSX5ObGHgE3l1oDb03eeYrunyqrr6VeaR6Hvu+5eGIkpZbe1uuvg7kRst6D/+nQriDB3+dbY+1vsNFqAHkVe5Xf87oO44JAmV53b0vRnrTklXIISD4DYPdzedDX31J7AEmPMUgARmQkcBwRXJ9u+CGYd5F029BQYex40VMEbAc1IRp7Fln7f5eE35jJj8wXU1jd6JoU9svkoni07gNFFW3l6yn3xAdFxF8PgY61jfxDQhGmXK6D/DNj6Ccy7KH79xBugzz6w8T2YH9CEacqd0GMSrJsFCwOaMO15H5TuBKuegS8DGuHs/Q8oGgIrHofFAU2Y9nsS8nvD0oesPz8HPQ85hfDV3fBNQBOmGW9Y/7+4FVb7mjCFC+Bgu9Lop7+D9b4mTHm9YH+7CdMnl8Km2ZTXNLByTRkzR8La+t78YuX/0bMwl9r3LySvYgHdl27mWIBZvbhhUD6Xrf4Z68pquGHQnxiZtxoRYaeFpcwcWcbA5Xtj3S5wx5BbGRDZFDt0n0g+k/oPp7JuIsbA8eUXwqwaiqKGmSO30PObPJa/MYPK2tOsH+PrR0KjL61y0DEw3m6E47/vAIaeQl6fs8iXGs6u+yGnjqxntxXdGZZbRX04Ckt/DiPPgppNXBL5KeE8QV4t5S8DgW+gIXIOUMqAyEbuGHIbPWf39DZ/sO+94bmruKvf7YA1oOe+YTVLYpcrCIcmMyF/Kb/v+zeYZblFrizYzvaR9SwLX05NfQmTC79g/Gc3wVdN9/YNpdu4YtuPKC3Ym0G17zJz5L2xdTmv3cLMkZu5bPUFLK0dzPSSOfykz1PWV/rmzcwcaSmRX6y8mLX1fTim21v0++AmyA3zm0gZkfwQzCrhnL1+x7TxO1Gy5lFmjvyjdck+6s7MkdsAOGvZ1ZRV13NGr+c4dfvvYFZTe9eZIzdz2tKbAPhJ7/8wvfQDmGWd9+W51Zw8vJGBA17n/aWb+Vnfx9ht0QJmjjQMnNsTwhK790ICx8ufGRKZTyQ/xMDP85k5cnvs3utemMtp4dvpVvgZjeWG7460r0F4Jx7jSqJRGLL458wc2eQuGrGliJq6XYHjrQXvnRFbv/OSbrAqB3rvDZOsBmC8fSLUWokaE6vrmTlyO7MrJxGWvQB4aPhvyQ/ZVs6sW/h1Thnjek/mL5tOsK/FJbF17nuvuXHPufd4J7uaMA0CVrrer7KXxRCRs0VkrojMra8Prs/THJGw8MTclQjWTN8hPQsZ3quIEb2L+c6eQ/nloWOprGtIWDROaR1BDWeCrvH1z3/Bo3NWJNyP29LICUnsaa0+GvWksLpxnuIqbFeGkyHkDMlbKmr590erqLDdRq3FcUU45xUOCUJ8RkyjMXGxBvcT/fgBpQnLr7sX+wPfjn/dvW/nqd890zvHN7nB2aZbQcST0hwKCUk7TiUw7pzFjdGm87z0yPEcPK4v7gdq/64dC82fwZWMAd0L2GtkT8b2K4lZjo5lF3SNq+oaqKxtsNb5rnFRbhgh3oKKhEOxeEidz9coIgnnCEUSzHnxU5AbTpjl1tlVb7tUEyYROQk4whjzY/v9mcBexpgLgrZvSxOmmvpGj6/Rz/grX+T0vYZyxTEd7h3Lesqq63luwVoue+pTHvvJNPYe1YvqukYKcsP86l/z+ZcvXdNh+U1Hx5r3fH3DUYy67HnA8i9vrqijvLaB0X2LefTHe7HXDa/yu+N34eqng0tL/+aIcfz+xS+5+NCx3PbKV9xx6kS+vftgqusaGX9VU4G9/Ub3pqqugf+ct2+rzrW8pp5dr3459n7+VYfxqyfn882WKl686IDY8n1veo29Rvb0NEYyxjDiUuscn7twP3YeGB9ABZh2w6sxxTmkZwFv//qQ2LrnP13LeY9+xOE79+O+My1//aG3v8niDRX8/sRdY720l914lGeQGnXZ8zRGDR9cNp3q+kbuf2spP58xhvxImNL8SGCjJ4CPrjyUyb4mRwC/O34Xzpw2jCm/e4Ujd+3PdcfvGltXVlXPxGtfpiAS5rkL9+OQ296MrXvinL055b7ZPPSDPTjI5XYLOr7TaMnNo3NWcPlTCxNuc9yf34mVej9opz787JDRnHjPbM/25z4yL1aZ1uGSI8exdGMFb321iXMOHMk1zzQ5P24+cTcMJnZt3fft/N8elnQS3pylmzn1/vcZ17+Ea761M6fe/75n/asXH8hp978fF7hPdP6pkqwJU1dzT60GhrjeD7aXtTvJFAZYgcktCXyJSsuYeE3TILqlso65y7dw0r2zeeRHewW29QzCHWPaUllHz+Jcymsb6FYQiVkGG7bXJHwqcwKnFb7sKP/D3Zpt1bFsrNbgv6+K8sKxNM/NFbWxyWxVdQ0U+bKS3IN4MmvH/fDs38cIe8LX8ZOaDHTHwivJj3DpkeN46uPVcU+1znUrLYjQtzSf67+9K8nYeWApn63ZnrAG0pX/XUhYhO019ZT4KvwW5+cwrFchvzp8pzhL4BS7NIb/M6mSyDpzcJ93z8LgZlpBy4rzrOoBhviYRtQ0lYP3WxalzUwSdbLMSguCe7Rc8dTCQIXRkXQ199SHwBgRGSEiucBpwNPpEKRnUW7Cap5K6vj7Os9Ztjn2FPfp6rKEqbHgLWHt7mewvaYhlo1Tmp8TG6j9Py53loozCDtzNorzgpXG0k2VvL14E63F71bJCYcIhaw+ElOum8WSDeXUN0bZWlUfF4R1U5DkocY98BX4Ym7jB5Sy8JrDPXMwKlwpqeccOMpj8Tg4fTjyUsgaA9h/jDU/Zc8bXk24zWVPfUp9o4krCx8OCW/+6mCO2W1gwkG+uWQEd2VhN815tdzrSwsinuPvaVfcDRLJSue1sqri06mh2u7L4b+2zbVwdWb1l9r7d9ipn5VyPHvp5sDPOXW5OoIupTSMMQ3ABcBLwBfAE8aY4NzJDqZHUW6zJRCU5lm51duH4OHZK3jva+uHUNcQ9VQB9ePufLfRN9Gq0B70B/UoIBwScnNCcZOxurkyZ/xlQQoDym60B0GDhPsYKzZXxfo2vPVVYuXkVwZu3E+k+Tnx2/mtlMoUJiw+cc7ePH3Bvin3qQ4qs3LGtKHcdEK8hZKsa2Giw/ktKD/uHibe/SWX3/1dFOWFPdfyt8daruigJ34nM8sYE9eK12DYXlNPTkgS9iJJhCPOhAGlHoV2xrShST/3r5/u3aLjtIQupTQAjDHPG2PGGmNGGWOuT5ccvYpyA2vPKPGsK6vhtpcXxaWV1jY0crM9W9aN05jmjllfJd2vW2n7C8ydNGUwF80Yw6+PGAdYSsBvaXR3+ZL95T6cAbS9lUYQ7mPk5oRYbHev81thbpJZGu7BxT1pLhFOkDaZy6dXcR67ueaCNEdpgJ++W0EkMO052TVOVOfKn7X4/qXT+fN3d4+9710cnPDQ3PfpXl+Ym+NRWo6iCnZPRWLVhctr4y0NZ+5PqkrXYfq4vvzy0LGcfeAoz7XoVpg45fj0vYZ22MQ+6IJKI1PoUaiWhoMxhj+/tpgv18VbBS99to79b36NP722hOWbvVbFzS8u4rkFa1t9XHdmld9V2Lsol4tmjI25Pgoi4bgZvN08SsNnacSURqvFSxn3MXJCodigk6wooj+7ybu/ph3mBVgaiWjOv54qOSEJVGp5OWGOnzTQM7kNmlOAidxTXln7d8tndN+mCYmJBufmvk+vkvAWO3TchUG7tmIaBM7TcKyP5opcBpETDnHh9DGxmIlD9yTB846qbuugSqOV9CrOpbKuMeXewNnMS5+t59aXv+LOVxZ7ljc0RjnnH/Oot1MQ/f58d9ey1uDug+G3IvwzhtfaEzPddC9MrDSKY4Hw+BHi6N0GxC1rC15LQ2JppUFP66ngFjkvBUvDobXBZT9FecEl2HPCQkl+hFtOnui53sniJImURtBnkikfB/fujp8UX/HWfbzp4/t5nu6TWRqxmEbUxIowOhjb0nArjVd+cQDvXXKIfzdJcR83mXuytfdNynJ06N6zGEeb+2+QHY3tNfX89JF5AHGlmJ3igA7+khKpZkZd/+1d+OLaI1h2o3dSktt37HdPJXvaclppumcV+8uCJAtCX3zo2ITrWoIziLgVU04oxN4jrWZKTqvZluK+zEExjUSk4spKBf9TscNJk5ssDPfzw6i+xXHbBm3nJkiZJxtIm/bX9LmbTtwtfr19CWaM78eQnoWe4xf46kEdMLapGKVT16qqrjGWsuvgWB8lLhfomH4lsaKEqeKeHZ9snoo/saC9UaXRSpxBaUef4HfafU154+77+IG3lzLjdm8P5saoVdfpgbeXBhaFS8S+o3oHTm5yWxrrfMXg/D8cp0wIWEHCJ3+6t6/0hveY/oZHblIZnJLhuGce+dFeccdeubUqFuDfZVBwBlBzuK3fliiClvrbE+GkEbt5/sL96evK6HGe4EMCY/slLj7YkphSc0UkwXuOQdaKu3mW//ihmMzW/0KXZVNkK8qgSXxR45SFadt945Yl2f3Z0e6prjZPI2MoVaUBNFkTueGQp4/zdc99Ebdt1MBlTy3kmflr2GVQt7gZ4HsM78GHrho9Dv27BacPun3HTjloB//A7naHDOpRwMg+xZ5S1S0ZMFvy9B7ELSdP9NQfcrtALvjnx4D1VNvaQbzaozSal/WAsX14K0HRu9aQHwnHBbD9A6azPlFqrENQKfdE5CYZSGP7c882D7i+zrJkiRDONoW5YfYb3Zt3lmyKzQgPwtjzNFL5LpLL3nSARAkCkDwbrT1QpdFKYu6pmh1XaXyzuYqqukauOmYCt7y0qNnmMFFjWL7JUjL+p7y/fG8qh07oFzizN9GPLZlr0J9d434yc4LDeeGmbdy/wXMOHJlwv9B2S8NP0ODVFldRTX3ThLpU5lXcd8YUj6JpK7nhUFz5D/81C7kG3mS0JBEhlZIczWdPWf+dQHvQ4OzcSgW5YW4/dSIr7ASPRPs2xlLkqcRcUpENkp+ruqcyFCfTZEe2NJzud/uP6W1ljtim+Xy794CfqDGxLCf3j3Hy0O4c6nIfuXnt4gMTHj/ZtfdbA0G/Z3eQ2P2DT9arAVKf4JYqQQNjKqXXE1Hjqg2Vl8JAVZAbTliTqzVEwqG4LnX+eRXO9S5oxqXUEvdUKpZZc0rImfnuKLOgXboVXt+SfPYY7kz6C965MyO8rQ8bXksjfe4pVRqtxPlilm2sbGbL7GXRunLyIyFG9ikmJEKjMUSjhuPuejdwe6ufhaU03HNcBvdInFPeqyhxTwCnfHUQfrdGoNLICVYazWUctZfvP+jYDm1RGp5AeBufbltDbk6IXr55Ev6nbOehobAZ+ZK5YVpDc9+dU47GkTcoduDswa/w3Lv+4b4jYq8NlqXR1u/CW2SyyZoc6puT0ZrU3pagSqOVODGNP762JM2SdB6NUcOtLy1i5RbLHP9qfTlj+pYQDgmhkNUtLlk9rmi0aRb3Dx78ELB6IV973M6xbe48dRJH7Nw/9j7ZD2BbVV1c34fEJPZNA4jrl9CSuQ3tQaClkYJ/PhXa2ypKhUg4xNG7DvBMtvMrcedBuTk3XEv1884DS7nkyHEJ1zsKOpF7p9ZWGvm2VRD0PdTZ9bT8rjX3KZ570CjOP3gUYP1uauqjbY9p2AfICYnnvv/f+d7ime2VOp0IjWm0kmTZC9nK3OVb+PPrS/hi7XbuOn2ypwaTM7FpXVniNNqgLm63nzKR7q4fwPG7D+L43QfFYhvJAqGvL0o9eNt8zaHkWTUdSdDTb24CxXX8pIGMayZ47GZiC2Zxtxd5OSFEhGN2GxgL7PtxytU3p6Bzkrhhgnjuwv2Trnfug0RK2W9pBFmdTszIbz2576FuBREunD6Gu17/mmr7QamtMQ1HZsuSy2PB1YdRkpfj6Tc/8+xpbbJSU0GVRjuwobyGviUdVyAsU1iy0Spv0bMol7W2cnCsgpAIjVETW+6w/5je7D6kO398bYknu8qhPX3pyXDKiJ+2xxDP8uG9Cjlq1wEepdLZSiPIBZPoh3/nabsHLg9i5tnT2HVwcPn0jiSlgLR9zs1ZGsGB6Na7rJyBPVGsx+kV4gzwQcoltk2uP24m9n/rGjiFlyvrGux9tu2+cs7buTecgLczZ+OEyYOYZs/x6UhUabQD68tqs15pfLF2O/e88TVgTeJba8+u/t4+Vo/lkN032T1hb+eBpdx1+mQ++WYbENwsJlmv5yBeu/hAZn64kvvtarRBBA2439lzCJOHdWdcf+9T+hu/Ohjwzm1IJXjcngSNgXntYMmOTjJpriNJ5Um3ucE76f7bcG2kGUsj5p6yB/igci1OOCGRe8qxtJz3MUujjYHw7gURTpw8OK5YYVFeDm//+mD6dWBlWzeqNNrAtyYO5On5awLdLtnGkX94O/Y6JySssS2KQfasVqfCp3vC3uPn7O2ZHVxWFZ/tlCjT45aTdguMV4zsU0zfksTBcQi2FEQkTmF41yf/fEfS3oFwh7a6Q1rCZ9cczj/eX8FNL3yZkuu2yT3V/La7De7GAtcs65F9WlYp1o1zrRNd39qGeNfTzw4ZzUE7Nc3+NnbfwbDve2uKl4Ts99ZyZz5Se8Q0bjtlYuC6jixQGCdHpx0pCznVdnXsaPWnGqKGNbal4Uy8c9xT7ib3sQlS9l0WFCRP5NM+eeoQZiRIw/XHAMb1t1JkT51qfR+t+XFmXkyj9TI46eCdqTSK8nJibpJUZHdclalc66cv2I8Tdm9qGnXF0a3vltmc0qjzBcIBLj5sJ6YM6xl7n+gZ0fkWHSvG+V6rbPdUOjLZOgK1NNqAcxPUNAR3J8tWorbS6F2cFxv0nbLQ2wKsiXASS6M1uF3m5x40ijOmDePdJZvoX5rP43NXtmpinDflNv3uqba4YJ46f1/mLN3SotnU7YFTmNI9IP/pO7sHZsA5Hf1SHUhr7e2vOHo8e49qvd++uUC4P6YRhONljesvHqA0nXpUze2zK6FKow04g1N1XXZbGv4+GA1Rw7rtNQxwlfcIhazttgZYE86P6W/vLmsXedyD4WET+jGoewGnTB3Cu0usbK7WlPnw9KDo4u6pUX2KGdWn8+MZjiJwF9M7dmJ8JVloUjCpWnWxrKY2xgWkFe4pP45LtTg/eJ5Grm/+T2U7xTQyBVUabcC5sWobsldpbNheE9eyM2oMWyvrPJlPzuQ+Z5a2J0ff/jX5M6v++v3AvvXN4nbnuJ9Um3M9pLrPRJbG42dP65Ac+CCLoCumdDsuJ7+vP4h6W8GkOicmlcE8FZx4RKJ7xHk+SjbA//ZbE5g4pFusGrFD7P5zfXcCVNU62VPZoTS63p2ZQTgDVjZbGnNXxBcQ/OibrcxfVUYPV+ZTWIStVfUsWFXG8ZMGcsxuTU+Yibwkw3q1LqAZTqA0HHdBW2MSiT6/18heTBjYusqzyWjvMiLpwnHbpDJjPqY0UnQl1ta3j4vHsViau0eSuc1K8yN8b+/hcefpfI9uhR8SabdAeKbQ9e7MDCIW08jiQHiQu+ndJVbNqR4uS0OEWKXUQl9Do0R59a2txpmonWmDnQuZrKtdKiTrVdARSMBs9XTM5G4rjhszlXkUDTH3VGoDaV0LYyCJqE1ZabQ+LhbxxTSc5l/Z4p7qendmBuE89VTXZ28gPCiw7eBOiXX75YsSVDT109pqnJ4OZq5BxBmIUplcloz2ri3VHA3R+PunK1oaTj+LVArm1UdbZmnELIQ2TpBrsjSSD+CtSURw7hv3HBv3rdTZsbKOIjvOIk04TytZbWm4Cgt+evVhnj7SbkvD/XTpb4bTkpadqeD5Ifoa4QCMaaZKbabhPEW7aa/aU53JD/cbzhVHj+e7ew1tdtuGVgbC28vSSKSUf3vsBIb3KmzVg4PziUhO02dTbdHaldBAeBsIhYS8nJCnFHW24Z5bUZIf8SgHd0zD/SPz99tO5K5o7RO9e3/uQWfKsB7cd+YUz0SsroAzgLrpipZGXk6YH++fvBeJg3PGqSoBJwbS1gZYU4b1AODkqYMD1/9g3xH8wFWhtiUEpfO6lUZbZc8UVGm0kfxImJosDoRvrvDGNNwDttu95NYL/v7a7R0icP8Q/YrncFeF3K5CXcA8n66oNFqCacHkPnCl6LbRPTWidxHLbzq6TftIROA8jYD1XZ3svjM7gYJI2NMpLdvwN9NxKw33pC33cr+l4f6x/OrwndosUyeHHDqcZxesiVvWFd1TLcHJtEo1EN6Uopu518V5gIkExDTG9ktPHbCOIHO/gS5CfiTUrq0yM42N5YlLnbsDnu4n/hnjveU/3Cmy+7RhNq9DS7q5tRcd2Q1ta0CyQbZbGg6p1m3bd3RvoON7RbSFQPeUvTBZM7Guhrqn2kh+JJy1gfD6xiibbPfUZUdZjW2qYznnIUb0bppn4fxgTp06JBaQblqX2AppDe3dza05Prh8uqefeGewoyiNVCuz3njCrpx30KgOb2XaFpzU6SD3VCSLvs+MOxMRuUVEvhSRBSLylIh0d627VESWiMgiETk8jWLGyI+Es9bS2FRhuaau//YunH2A1YXMccVd+61dPNaFY034FQY0FSxMtL6ldLZruG9JfotLuLeEl39xQNyyTHbDtAfXHrczU4f1SFkJ5EfCjOmX2VlxjgvNP7kPILeNaeCZRCbema8AuxhjdgO+Ai4FEJEJwGnAzsARwN0ikvZ0hPxIiNosjWlsrbTcJr1cqbVOemiiujtFefFfibv7WlFuDr8/cVeevmDfuO1Spcl3nB0/xLH9SvjkqkM9y7I9pvG9vYfz5Ln7pFuMdqUuIJ03KM7R1cm4MzHGvGyMcfoXvg84uXHHATONMbXGmGXAEmDPdMjopiASztqUW6eOVNAkPH/lUucH45+jAd7BvTAvzKl7DGW3NrQhDWfjD9FnPu0o7qlswnmg8le5hey6VzM9pvFD4HH79SAsJeKwyl7mQUTOBs4GGDq0+UlGbSU/Es7a2lPba2ylEeBC8McmHLdVcYCl4fbntsePxzFc2tuF8/yF+weW8e4M/MF9VRpdD+fBKeJ6AAiqR9XVScsvRERmAUEJ9ZcbY/5nb3M50AA82pJ9G2PuB+4HmDp1aoe31MvfASyNIL+zf3B1Kv0GWRrt7Wpprrx1a+mIYoSp4q8Mm+3uqWzE+Qrdg05T5eXscKVCmpSGMWZGsvUichZwDDDdNDVzWA0McW022F6WVixLIztjGturE1sa/tRHpzxDUEyjvZ+y2lICPVPxZxFn07ntKDiKvzHapDb8LWCzgYw7ExE5Avg18C1jTJVr1dPAaSKSJyIjgDHAB+mQ0Y0VCO/6lkY0apj5wTee2cnbq+sRgZKAjCe/e6oupjTit3VSZC+aMaZdZM3KmIa6p7o8uwzuBsCug7rFrXMng3R1MjGm8WcgD3jFdkO8b4z5qTHmMxF5Avgcy211vjEm7aN1QZak3D4xdyWX/OdTyqrrOedAK712e00DJXk5geUPCn3F12qTBMKBdi3d0FzLzq6Ivxx7Np3bjsLBO/Xl7V8fzJCehbFljq6IqHuq4zDGjE6y7nrg+k4Up1nyI2EaooaGxmib+zikk1VbrZr/7pIoZdX1Cecn+Gs+OTGNIPdUexMrQZ1FT+OaPZUduBUGuCb8deGxwU/2nEmacPo51AQUnetKlMcypZqeI7ZX16fc88IpKFeUwNJoT4J6MWcD39mzKdsv285tR8VpL5tVrtR0C9DVcTp8dfW02+011tQYt5LYXhOvNG47eSK/mDE24X7aY8Z3czQ1W8qu2/fGE3aN9SvJpifTHZnGLLxXM8491dXIy5KWr06mlDtWUV7TEGdunzgluA+BQ2e4p+oarWudjU/jJfkRttc0ZE0/6R2dhmj7dJPMJFRptJGCbFEatnvKlS1ITX1jXMC7OVItdd0WYuUasujpzeGfP9mL17/c0CkWm9LxOEojmx5w9M5sI/kxpdHVYxqWe8pdqrqqrtHTgzsZ/z53H+at2NIhsvmpa8y+H6LDsF5FnNXKznFK5tEQUMSwq6NKo404g2pXT7t13FNupVFd15hyX+Mpw3rEWml2NM41H9S9oFOOpyitpTGqMQ3FhxMI7/ruqXhLo7oV7qnOYMb4vtx84m4ct/vAdIuiKEmp15iG4ic/S2IaFbW20rC9bHUNURqiJmX3VGciIpyyx5DmN1SUNONYGtkUf8ueM0kT+VninnJwLA0nhbigE+ZdKEq24iiNrjzx10/2nEmacNxTXbkRk9NxDJqURlW9ZXlkontKUboa6p5SYmRDINzJnAJYuHo7I/tsiXXry0T3lKJ0NXYo95SIFIrIlSLyF/v9GBE5puNF6xpkQ0yjsrZJafzj/RWcfO9sqmLuKVUaitJWIlmUHp7KmTwI1AJ72+9XA9d1mERdjGyIabgtDQfnfNQ9pShtJ5tSblM5k1HGmJuBegC7x0X2OOjaSDgk5IZDXXpyX2VdgNJwLA11TylKm8mmmEYqSqNORAqwuxiKyCgsy0OxyYuEurR76sm5q+KWqXtKUdqPbIpppBII/y3wIjBERB4F9gXO6kihuhoFkXCXVhqPz10Zt6w6lj2luRKK0layyT3V7IhgjHlFRD4CpmG5pX5ujNnU4ZJ1IfK7uNIIwul7ru4pRWk72RQIb1ZpiMgB9sty+/8EEcEY81bHidW1yJaWr26cTnzZ1B1PUdJFNsU0UvE9/Mr1Oh/YE5gHHNIhEnVB8iNdOxAeRKz8uCoNRWkzO1RMwxhzrPu9iAwB7uwogboi+VloaajSUJT2I5tiGq05k1XA+PYWpCuTHwlT20WVRqO765KL2oYoIpATyh6zWlHSRc6O5J4SkT9hp9tiKZlJwEcdKFOXoyu7p6rsORqRsFDf2KRA6hqj5IZDiGTPza4o6SIS2rEsjblYMYx5wGzgN8aYMzpUqi5GVw6EV9Zacl/9rZ09y+saouqaUpR2IpRFFnsqMY2/d4YgXZmunHLrWBr+ciG1DdFO6fetKErXIqHSEJFPaXJLeVYBxhizW4dJ1cXoyoFwR+6CiPdWqGuIarqtoihxJLM00lrJVkQuBm4F+hhjNonlXP8DcBRQBZxljMmI2IoVCO+aMQ0nFuMvF1LXqO4pRWkr3QoilFXXp1uMdiWh0jDGrOhMQdzYab2HAd+4Fh8JjLH/9gLusf+nnfxIiLrGKI1RQziDfZfnPjKP0X2LufiwnWLLHLdavk9B1DU0ZlVuuaKkg1cvPpBtVdmlNFLppzFNRD4UkQoRqRORRhHZ3sFy3QH8Gq977DjgYWPxPtBdRAZ0sBwpUdBFemq8sHAdf3ptiWdZdYLChBoIV5S207s4j9F9i9MtRruSyqjwZ+A7wGKgAPgxcFdHCSQixwGrjTHzfasGAe7KeqvsZWmnKzdiaoppqHtKUZTmSamEqTFmiYiEjTGNwIMi8jFwaWsPKiKzgP4Bqy4HLsNyTbV232cDZwMMHTq0tbtpEZne8nVbVR0Pvrs8cF3MPeVTGrX1UXVPKYoSRypKo0pEcoFPRORmYC2tm0kewxgzI2i5iOwKjADm25PKBgMficieWB0Dh7g2H2wv8+/7fuB+gKlTpwZPd25n8iLW5Tj9gTk8fvbe9O+W3xmHTZnLnvqU5z9dF7jOURpBgfCiPC2LriiKl1QG/zPt7S4AKrEG7hM7QhhjzKfGmL7GmOHGmOFYLqjJxph1wNPA98RiGlBmjFnbEXK0FMfSWLG5ij+9tjjN0sSzuaIu4brqBJaGptwqihJEKqPCFKx5GduNMdcYY35pjFnS7Kfan+eBpcAS4C/AeWmQIRD3gPvonG+SbJkeahoSpwM7Kbfx2VMa01AUJZ5U/A/HAneIyFvA48CLxpj4ptIdgG1tOK8NcH5nHLel+J/SMy31Nlkxxep6K7U2xxe/qFWloShKAM2OCsaYHwCjgX9hZVF9LSIPdLRgXQl/5lGmZVHV+iyN+978OlbdtrqukfxI/G1Q29Co7ilFUeJIaVQwxtQDLwAzsQoXHt+BMnU5/INuJimN/32ymmWbKj3LbnzhS5771AoH1dQ3xgXBATZV1LFqa3WnyKgoStchlcl9R4rIQ1jzNE4EHiA4XXaHxe+eShZD6Gx+PvOTwOVR29KoqW+Mk9/h7cXaCl5RFC+pxDS+hxXLOMcYU9vB8nRJ/BViM8nSSITTSay6vjHOvaYoipKIVEqjf6czBOnK+OczdAWl4QS5q+ujCS0NRVEUPxrpbAf8AeNM6eJnJZwFU1XXQFlVvRXTUKWhKEqK6JTfdsDfEjVT+oX7s6bcOLGO3QZ3o1dRbuA2Vx4zoSPEUhSlC5NKj/BCrJRbgEUa1whm+ri+FObl8Mz8NdQ0ZIjSSMHiSRYInzqsR3uLpChKFyehe0pEIiJyJ1YpjweBh4ClInKJvX5SJ8jXZfjrWXtw/sGjgNQG646mpr6RN77a0Ox2X62voL7RkveTqw7lohljYutywpkzQVFRlMwgmaVxG1AIDDPGlAOISClwq4jcAxyBVVxQsXF6ameCpXHTC1/y0HvLU9p21heWculemEvv4rzY8ohWuVUUxUcypXEUMMa4oqnGmO0ici6wCauTnuLCCYhngqWxZENFqz4XcsVnVGkoiuIn2agQNQHpN3ZPjY129zzFRSY1Ywq1svaV+2M5GVQ/S1GUzCCZ0vhcRL7nXygiZwBfdJxIXRennEhnzwivqW/kmD+9zYfLt8SWtSQc4S6D4lY2WrBQURQ/ydxT5wP/EZEfYtWbApiK1fL12x0tWFfEiWl0tntqyYYKFq7ezpX/XciLFx0AeN1MzeGep+H+nFoaiqL4Sag0jDGrgb1E5BBgZ3vx88aYVztFsi5IOCREwtLpgXCnYq0726kl7qnC3KbbwP2xiFoaiqL4SKWMyGvAa50gS1aQnxOmtj6KMYaGqOmUYHJD1LJsckJNxwq3wNL49RE7NX3OpTUiIVUaiqJ40VGhncmLhKlpaOSaZz5nzOUvsK0qcavV9qK+0bI0Ii5LoyVNoI7adUDstXiyp9Q9pSiKF1Ua7UxeToja+mhsjsSka1/p8GNuqbQUk9vSoAXjfY7Humid4lEUZcdAlUY7kx8JdXpM47xHPwK8MY3GxsTFCv24rYs8VyaVv6aWoiiKKo12Jj8STliwcEN5DWvLOq4bnjt+UttKxeVkgCmKogShVW7bmbycUFx12caoIRwS9rzeSjxbesNRrZ58lwx3DCJZhdtkaF9wRVGSoSNEO5OXE6aitsGz7Iu12z3vK+u869uLHI+l0aQ05lw2HYBUvE3akElRlGSo0mhn8iMhNld4M6acQLWD06TpH7OXM/yS52hobJ/JgMWu+RZu91S/0nxmnj2N5362f7P70FngiqIkQ0eIdiYvJ8xWO832yF36A9BoTKz8ODTVprryf58B8NJn69t0zD2GW30v3C4v/6z0aSN7MWFgKXecOjHpvuo6uQSKoihdC1Ua7Ux+JER5jeV+Gte/FICyqnqWbqyMbVNV18jwS56LvS+vqW/TMR1XVDRqPMv2GtGTl+yyIg6HjOsXe/3X709l/lWHedbv1L+kTbIoipLdqNJoZ9zZR44y2FpVFyv1AbBmmzeDql+3/FYfLxo1LFhVBlgWjUNtQyMjehfFKQF3/L1XcR7dCiOe9VoOXVGUZGTkCCEiPxORL0XkMxG52bX8UhFZIiKLROTwdMqYCPc8h/XlVmfcbVX1/OP9FbHlZ/9jrucz1XWtn9fx5bry2Gu3pVFTHw3MhHJP2MtVBaEoSgvJuJRbETkYOA6YaIypFZG+9vIJwGlYxRMHArNEZKzd3yNjcGcfXXjIaN5ctIGy6nq+Wt80uOeGQ9Q3Nontz7ZqCe6MKL+lEZQJ5a5iq0FvRVFaSiaOGucCNxljagGMMU6j6+OAmcaYWmPMMmAJsGeaZEzIJ99si70e3KOQ7oW5bKuqY9/RvWPLK23L4vhJAwGoqGmfFNzK2gY2ltdijKG2oXlLI9GcjHcvOYR3fnNwu8ikKEp2kYlKYyywv4jMEZE3RWQPe/kgYKVru1X2Mg8icraIzBWRuRs3buwEcb184GqElB8JUZyXQ0VtA72Lc+O2PWtfq8X6dl8gfEtlHafcNzsu9hGEOytr1hcb2OP6WdQ3Goyxiif6cVsaiZTGoO4FDO5R2OyxFUXZ8UiL0hCRWSKyMODvOCyXWU9gGvAr4AlpQREkY8z9xpipxpipffr06aAzSMwlR46LvRYRcnNC1DWawLjFxMHdyMsJxa177INv+GDZFh6evSLuM27Wb6/hW39+N275njfMAoKVgjsQXpIfiVuvKIqSjLTENIwxMxKtE5Fzgf/Y/ck/EJEo0BtYDQxxbTrYXpZRDPE9oeeGQ9TWN7JgdVnctiJCYW6YKp/ScGpXNRdz+HpDReDybVWW5RKkNNz6193mVVEUJRUyLhAO/Bc4GHhdRMYCucAm4GngnyJyO1YgfAzwQbqETMT4AVaK6zkHjAQgkiO8u2Rzwu0LImGqbSURjRqu+N9C/jnnG+uzzdSnymtm0G+u+KBWsVUUpaVkotL4G/A3EVkI1AHft62Oz0TkCeBzoAE4P9MypwBG9inm82sPj7VQbW7eQ0FuOOaeWl9eE1MY0Hy7VfegHxKI+qqhN6dUFEVRWkrGKQ1jTB1wRoJ11wPXd65ELcfdc7s5pVGYm8PbizfyxqINDOnpdW01V3G2uZIfiT4/oncRxXkZ99UritIF0EfRDqbR//hv092eiV0QCbO9poGzHvwwVn7EoTn3kruS7YBuBXHrg7KnAF7/v4N45mf7Jd23oihKEKo0OpgVmysDlz93oVVxtiC3aWD3z9eImuTd9+56bQkA954xmcE9ApSGTt5TFKWdUR9FB+N3Ax25S3+WbKhgUHdrkC90KY0z/jrHs61pRmk4c0IGdCsI7OetXfgURWlvVGl0MP6Yxj1nTPG897uk3CRybfkJiSRQGmppKIrSvuio0sG4M5iC5kW8s2RTws+mqDMY0aco0JWl8zAURWlvdFTpYAoiTcbcLgO7teizzcU0HIrzcgKtEnVPKYrS3qjS6GCcp/3jJw3kr9/fI2799HF945Z1K7Ayq5K5p/zrgpWGfr2KorQvOqp0MIdOsDrlnXPgqLiGRwB3nT6ZIlcw/BczxjLnsulAcvfUpopaz3u1NBRF6QxUaXQwx00axPyrDmP8gNLA9fmRMCdMHhx7v2xTRaxHRjL3lLu6LUBjwKaFeao0FEVpX1RpdAJBFoYbd+bTqXsMJWxrjSDrYVNFLQ2NUWrqvUoj6tv2pCmDtXWroijtjo4qGUCOS2nsOrhbTIn4LY3K2gamXjeLa575nBq7yKGzrVvBiMCtJ0/saLEVRdkBUaWRAYTDTUqjOC8nVojQbz04bWFf+mxdrDLug2dZwXW3gkkx6UpRFKXFqNLIAHICJuaFQ+Lp+Q3QYCuRcEhilXGL7LjFis1Vse2O2Ll/R4mqKMoOjs4IzwAcZXDeQaNiy4JKnUfdSsO2NPLtooTO+6N3G8Dtp6hrSlGUjkEtjQzg6U/WAHD3G1/HloVE4txTbkvDiWkU+CrZnr3/SE21VRSlw1ClkQGsLasBYJdBTWm54ZDEBcIbo1bGVEia3FNO7479x/QGYGSfog6XV1GUHRd1T2UAPYty2VJZx7d3b5qvERLBNxUj1j9j2aZKrnnmc6DJ0rjr9Ml8ubackvzk6b2KoihtQS2NDOCB708FYMb4ppIiVkzDa2nUu2bwxWIaudZXWJofYc8RPTtaVEVRdnDU0sgAJg/twfKbjvYsC3JPvfblhrjP5uoEPkVROhEdcTIUyz3lVRp/fHVx3HbOnA5FUZTOQJVGhhIKsDQURVHSjSqNDCUsQjTa/HaKoiidiSqNDCUkxM0I9+OUXVcUReksVGlkKKGQsHxTJR99szXhNoW5OolPUZTORZVGhhISYe6KrZxw93uxZeP6l3i2CQfUrFIURelIVGlkKEEKocqeBe4gqNJQFKVzyTilISKTROR9EflEROaKyJ72chGRP4rIEhFZICKT0y1rRxJkRDgT+pJtoyiK0pFknNIAbgauMcZMAq6y3wMcCYyx/84G7kmLdJ1EKGD+RU2dX2mo1lAUpXPJRKVhAKdyXzdgjf36OOBhY/E+0F1EBqRDwM7A754yxlDlszQURVE6m0wsI3IR8JKI3Iql1Paxlw8CVrq2W2UvW+v+sIicjWWJMHTo0I6WtcPwWxH1jSZuhrgaGoqidDZpURoiMgsIai93OTAd+IUx5t8icgrwV2BGqvs2xtwP3A8wderULjulOuSzAZ1S6L+YMZaQwG2vfJUGqRRF2dFJi9IwxiRUAiLyMPBz++2/gAfs16uBIa5NB9vLspKwz4xwguB9SvLSIY6iKAqQmTGNNcCB9utDAKdK39PA9+wsqmlAmTFmbdAOsoGQK6ZhjOGVL9YDkJeTiV+Zoig7CpkY0/gJ8AcRyQFqsOMTwPPAUcASoAr4QXrE6xzcMY3fv7iIe9+0WsHW+zszKYqidCIZpzSMMe8AUwKWG+D8zpcoPRhX3SlHYYDV5W9TRV06RFIURclI95QCcZlSDjPGa5FCRVHShyqNDKUhQGkM6l5AKCR8tb4cgNlLN3e2WIqi7OCo0shQGhrjlUZ+xPq6hvYsBGDqMO0JrihK55JxMQ3FoiGgA1NhrvV1/XC/EcwY348hPQs6WyxFUXZwVGlkKEHuqQJX/4yhvQo7UxxFURRA3VMZS5B7qiCiTZcURUkvqjQylGD3lCoNRVHSiyqNDCUo5bY4T72JiqKkF1UaGUrQNI3ifFUaiqKkF1UaGUpQ1fMcbdWnKEqaUaWRoTSaeFPj5c/Xp0ESRVGUJlRpZCjbqurjlp2+V9dtKqUoSnagSqMLMaxXUbpFUBRlB0eVRoby/IX7s+/oXp5lmnKrKEq6UaWRoUwYWMp5B432LNPJfYqipBtVGhlMXYN3gl++Kg1FUdKMKo0MZnuNNxheVh0fHFcURelMVGlkMJW1jZ73EwaUpkkSRVEUC1UaGczxuw/kxMmDY++7FUTSKI2iKIoqjYymMDeH206ZGHsf0hnhiqKkGVUaiqIoSsqo0lAURVFSRpWGoiiKkjJaa7sL8NAP9qC8piHdYiiKoqjS6AoctFPfdIugKIoCqHtKURRFaQFpURoicrKIfCYiURGZ6lt3qYgsEZFFInK4a/kR9rIlInJJ50utKIqipMvSWAicALzlXigiE4DTgJ2BI4C7RSQsImHgLuBIYALwHXtbRVEUpRNJS0zDGPMFgEjcZLXjgJnGmFpgmYgsAfa01y0xxiy1PzfT3vbzzpFYURRFgcyLaQwCVrrer7KXJVoeh4icLSJzRWTuxo0bO0xQRVGUHZEOszREZBbQP2DV5caY/3XUcY0x9wP3A0ydOjW+0baiKIrSajpMaRhjZrTiY6uBIa73g+1lJFmuKIqidBKZ5p56GjhNRPJEZAQwBvgA+BAYIyIjRCQXK1j+dBrlVBRF2SFJSyBcRL4N/AnoAzwnIp8YYw43xnwmIk9gBbgbgPONMY32Zy4AXgLCwN+MMZ81d5x58+ZtEpEVbRC1N7CpDZ/vKFSulqFytQyVq2Vko1zDEq0QY9TtnwgRmWuMmdr8lp2LytUyVK6WoXK1jB1NrkxzTymKoigZjCoNRVEUJWVUaSTn/nQLkACVq2WoXC1D5WoZO5RcGtNQFEVRUkYtDUVRFCVlVGkoiqIoKaNKI4B0lGEXkeUi8qmIfCIic+1lPUXkFRFZbP/vYS8XEfmjLd8CEZns2s/37e0Xi8j3WyHH30Rkg4gsdC1rNzlEZIp9nkvsz8ZVrWyBXFeLyGr7mn0iIke51rWoxL49cXSOvfxxexJpKnINEZHXReRzu9z/zzPhmiWRK63XTETyReQDEZlvy3VNsn2JNdH3cXv5HBEZ3lp5WynXQyKyzHW9JtnLO+3etz8bFpGPReTZtF8vY4z+uf6wJg9+DYwEcoH5wIROOO5yoLdv2c3AJfbrS4Df26+PAl4ABJgGzLGX9wSW2v972K97tFCOA4DJwMKOkANrhv80+zMvAEe2Qa6rgf8L2HaC/b3lASPs7zOc7LsFngBOs1/fC5ybolwDgMn26xLgK/v4ab1mSeRK6zWzz6HYfh0B5tjnFrgv4DzgXvv1acDjrZW3lXI9BJwUsH2n3fv2Z38J/BN4Ntm174zrpZZGPHtil2E3xtQBThn2dHAc8Hf79d+B413LHzYW7wPdRWQAcDjwijFmizFmK/AKVl+SlDHGvAVs6Qg57HWlxpj3jXUnP+zaV2vkSkSsxL4xZhnglNgP/G7tJ75DgCcDzrE5udYaYz6yX5cDX2BVYE7rNUsiVyI65ZrZ511hv43YfybJvtzX8Ulgun3sFsnbBrkS0Wn3vogMBo4GHrDfJ7v2HX69VGnEk3IZ9nbGAC+LyDwROdte1s8Ys9Z+vQ7o14yMHSV7e8kxyH7dnvJdYLsH/ia2C6gVcvUCthljGtoil+0K2B3rKTVjrplPLkjzNbNdLZ8AG7AG1a+T7Ct2fHt9mX3sdv8N+OUyxjjX63r7et0hInl+uVI8flu+xzuBXwNR+32ya9/h10uVRuawnzFmMlZ3wvNF5AD3SvvpJO350Zkih809wChgErAWuC1dgohIMfBv4CJjzHb3unReswC50n7NjDGNxphJWNWq9wTGdbYMQfjlEpFdgEux5NsDy+X0m86USUSOATYYY+Z15nGToUojnmTl2TsMY8xq+/8G4CmsH9N626zF/r+hGRk7Svb2kmO1/bpd5DPGrLd/6FHgLzR1eWypXJux3As5vuUpISIRrIH5UWPMf+zFab9mQXJlyjWzZdkGvA7snWRfsePb67vZx+6w34BLriNsN58xVjfRB2n99Wrt97gv8C0RWY7lOjoE+APpvF7JAh474h9W5d+lWMEiJzC0cwcfswgocb1+DysWcQveYOrN9uuj8QbhPjBNQbhlWAG4Hvbrnq2QZzjegHO7yUF8MPCoNsg1wPX6F1g+W7B6zLuDfkuxAn4Jv1vgX3gDi+elKJNg+afv9C1P6zVLIldarxlWZevu9usC4G3gmET7As7HG9h9orXytlKuAa7reSdwUzruffvzB9EUCE/b9UrLwJzpf1iZEV9h+Vov74TjjbS/rPnAZ84xsXyRrwKLgVmum0+Au2z5PgWmuvb1Q6wg1xLgB62Q5TEst0U9ln/zR+0pBzAVWGh/5s/YVQlaKdc/7OMuwOqv4h4QL7ePsQhXlkqi79b+Dj6w5f0XkJeiXPthuZ4WAJ/Yf0el+5olkSut1wzYDfjYPv5C4Kpk+wLy7fdL7PUjWytvK+V6zb5eC4FHaMqw6rR73/X5g2hSGmm7XlpGRFEURUkZjWkoiqIoKaNKQ1EURUkZVRqKoihKyqjSUBRFUVJGlYaiKIqSMjnNb6Ioih8RacRKtXQ43hizPE3iKEqnoSm3itIKRKTCGFOcYJ1g/baiQesVpSuj7ilFaQdEZLjdk+BhrAlcQ0TkHhGZ6+7PYG+7XERutPszzBWRySLykoh8LSI/dW33KxH50C6W5/R3KBKR58Tq+7BQRE7t/LNVdmTUPaUoraPArogKVqmIXwBjgO8bq1Q2InK5MWaLiISBV0VkN2PMAvsz3xhjJonIHVg9G/bFms27ELhXRA6z97cn1uzjp+0iln2ANcaYo+1jdOuEc1WUGKo0FKV1VBurIioQKz++wlEYNqfYZe5zsJoiTcAqUwFWCQ+w4iLFxup5US4itSLSHTjM/vvY3q4YS4m8DdwmIr/HKinxdgecm6IkRJWGorQflc4LERkB/B+whzFmq4g8hGVJONTa/6Ou1877HCzr4kZjzH3+g4jVWvQo4DoRedUYc227noWiJEFjGorSMZRiKZEyEemH1SelJbwE/NDuh4GIDBKRviIyEKgyxjyCVUl3crKdKEp7o5aGonQAxpj5IvIx8CVWZ7R3W/j5l0VkPDDbSsaiAjgDGA3cIiJRrIq/57ar4IrSDJpyqyiKoqSMuqcURVGUlFGloSiKoqSMKg1FURQlZVRpKIqiKCmjSkNRFEVJGVUaiqIoSsqo0lAURVFS5v8Bqd699xLTMFcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "random.seed(0)\n",
    "np.random.seed(0)\n",
    "env.seed(0)\n",
    "torch.manual_seed(0)\n",
    "replay_buffer = rl_utils.ReplayBuffer(buffer_size)\n",
    "agent = DQN(state_dim, hidden_dim, action_dim, lr, gamma, epsilon,\n",
    "            target_update, device)\n",
    "return_list, max_q_value_list = train_DQN(agent, env, num_episodes,\n",
    "                                          replay_buffer, minimal_size,\n",
    "                                          batch_size)\n",
    "\n",
    "episodes_list = list(range(len(return_list)))\n",
    "mv_return = rl_utils.moving_average(return_list, 5)\n",
    "plt.plot(episodes_list, mv_return)\n",
    "plt.xlabel('Episodes')\n",
    "plt.ylabel('Returns')\n",
    "plt.title('DQN on {}'.format(env_name))\n",
    "plt.show()\n",
    "\n",
    "frames_list = list(range(len(max_q_value_list)))\n",
    "plt.plot(frames_list, max_q_value_list)\n",
    "plt.axhline(0, c='orange', ls='--')\n",
    "plt.axhline(10, c='red', ls='--')\n",
    "plt.xlabel('Frames')\n",
    "plt.ylabel('Q value')\n",
    "plt.title('DQN on {}'.format(env_name))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 0: 100%|██████████| 20/20 [00:03<00:00,  6.60it/s, episode=20, return=-818.719] \n",
      "Iteration 1: 100%|██████████| 20/20 [00:03<00:00,  5.43it/s, episode=40, return=-391.392]\n",
      "Iteration 2: 100%|██████████| 20/20 [00:03<00:00,  5.29it/s, episode=60, return=-216.078]\n",
      "Iteration 3: 100%|██████████| 20/20 [00:03<00:00,  5.52it/s, episode=80, return=-438.220]\n",
      "Iteration 4: 100%|██████████| 20/20 [00:03<00:00,  5.42it/s, episode=100, return=-162.128]\n",
      "Iteration 5: 100%|██████████| 20/20 [00:03<00:00,  5.50it/s, episode=120, return=-389.088]\n",
      "Iteration 6: 100%|██████████| 20/20 [00:03<00:00,  5.44it/s, episode=140, return=-273.700]\n",
      "Iteration 7: 100%|██████████| 20/20 [00:03<00:00,  5.23it/s, episode=160, return=-221.605]\n",
      "Iteration 8: 100%|██████████| 20/20 [00:04<00:00,  4.91it/s, episode=180, return=-262.134]\n",
      "Iteration 9: 100%|██████████| 20/20 [00:03<00:00,  5.34it/s, episode=200, return=-278.752]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "random.seed(0)\n",
    "np.random.seed(0)\n",
    "env.seed(0)\n",
    "torch.manual_seed(0)\n",
    "replay_buffer = rl_utils.ReplayBuffer(buffer_size)\n",
    "agent = DQN(state_dim, hidden_dim, action_dim, lr, gamma, epsilon,\n",
    "            target_update, device, 'DoubleDQN')\n",
    "return_list, max_q_value_list = train_DQN(agent, env, num_episodes,\n",
    "                                          replay_buffer, minimal_size,\n",
    "                                          batch_size)\n",
    "\n",
    "episodes_list = list(range(len(return_list)))\n",
    "mv_return = rl_utils.moving_average(return_list, 5)\n",
    "plt.plot(episodes_list, mv_return)\n",
    "plt.xlabel('Episodes')\n",
    "plt.ylabel('Returns')\n",
    "plt.title('Double DQN on {}'.format(env_name))\n",
    "plt.show()\n",
    "\n",
    "frames_list = list(range(len(max_q_value_list)))\n",
    "plt.plot(frames_list, max_q_value_list)\n",
    "plt.axhline(0, c='orange', ls='--')\n",
    "plt.axhline(10, c='red', ls='--')\n",
    "plt.xlabel('Frames')\n",
    "plt.ylabel('Q value')\n",
    "plt.title('Double DQN on {}'.format(env_name))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 0: 100%|████████████████████████████████████████| 20/20 [00:10<00:00,  1.87it/s, episode=20, return=-708.652]\n",
      "Iteration 1: 100%|████████████████████████████████████████| 20/20 [00:15<00:00,  1.28it/s, episode=40, return=-229.557]\n",
      "Iteration 2: 100%|████████████████████████████████████████| 20/20 [00:15<00:00,  1.32it/s, episode=60, return=-184.607]\n",
      "Iteration 3: 100%|████████████████████████████████████████| 20/20 [00:13<00:00,  1.50it/s, episode=80, return=-200.323]\n",
      "Iteration 4: 100%|███████████████████████████████████████| 20/20 [00:13<00:00,  1.51it/s, episode=100, return=-213.811]\n",
      "Iteration 5: 100%|███████████████████████████████████████| 20/20 [00:13<00:00,  1.53it/s, episode=120, return=-181.165]\n",
      "Iteration 6: 100%|███████████████████████████████████████| 20/20 [00:14<00:00,  1.35it/s, episode=140, return=-222.040]\n",
      "Iteration 7: 100%|███████████████████████████████████████| 20/20 [00:14<00:00,  1.35it/s, episode=160, return=-173.313]\n",
      "Iteration 8: 100%|███████████████████████████████████████| 20/20 [00:12<00:00,  1.62it/s, episode=180, return=-236.372]\n",
      "Iteration 9: 100%|███████████████████████████████████████| 20/20 [00:12<00:00,  1.57it/s, episode=200, return=-230.058]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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   "source": [
    "class VAnet(torch.nn.Module):\n",
    "    ''' 只有一层隐藏层的A网络和V网络 '''\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim):\n",
    "        super(VAnet, self).__init__()\n",
    "        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)  # 共享网络部分\n",
    "        self.fc_A = torch.nn.Linear(hidden_dim, action_dim)\n",
    "        self.fc_V = torch.nn.Linear(hidden_dim, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        A = self.fc_A(F.relu(self.fc1(x)))\n",
    "        V = self.fc_V(F.relu(self.fc1(x)))\n",
    "        Q = V + A - A.mean(1).view(-1, 1)  # Q值由V值和A值计算得到\n",
    "        return Q\n",
    "\n",
    "\n",
    "class DQN:\n",
    "    ''' DQN算法,包括Double DQN和Dueling DQN '''\n",
    "    def __init__(self,\n",
    "                 state_dim,\n",
    "                 hidden_dim,\n",
    "                 action_dim,\n",
    "                 learning_rate,\n",
    "                 gamma,\n",
    "                 epsilon,\n",
    "                 target_update,\n",
    "                 device,\n",
    "                 dqn_type='VanillaDQN'):\n",
    "        self.action_dim = action_dim\n",
    "        if dqn_type == 'DuelingDQN':  # Dueling DQN采取不一样的网络框架\n",
    "            self.q_net = VAnet(state_dim, hidden_dim,\n",
    "                               self.action_dim).to(device)\n",
    "            self.target_q_net = VAnet(state_dim, hidden_dim,\n",
    "                                      self.action_dim).to(device)\n",
    "        else:\n",
    "            self.q_net = Qnet(state_dim, hidden_dim,\n",
    "                              self.action_dim).to(device)\n",
    "            self.target_q_net = Qnet(state_dim, hidden_dim,\n",
    "                                     self.action_dim).to(device)\n",
    "        self.optimizer = torch.optim.Adam(self.q_net.parameters(),\n",
    "                                          lr=learning_rate)\n",
    "        self.gamma = gamma\n",
    "        self.epsilon = epsilon\n",
    "        self.target_update = target_update\n",
    "        self.count = 0\n",
    "        self.dqn_type = dqn_type\n",
    "        self.device = device\n",
    "\n",
    "    def take_action(self, state):\n",
    "        if np.random.random() < self.epsilon:\n",
    "            action = np.random.randint(self.action_dim)\n",
    "        else:\n",
    "            state = torch.tensor([state], dtype=torch.float).to(self.device)\n",
    "            action = self.q_net(state).argmax().item()\n",
    "        return action\n",
    "\n",
    "    def max_q_value(self, state):\n",
    "        state = torch.tensor([state], dtype=torch.float).to(self.device)\n",
    "        return self.q_net(state).max().item()\n",
    "\n",
    "    def update(self, transition_dict):\n",
    "        states = torch.tensor(transition_dict['states'],\n",
    "                              dtype=torch.float).to(self.device)\n",
    "        actions = torch.tensor(transition_dict['actions']).view(-1, 1).to(\n",
    "            self.device)\n",
    "        rewards = torch.tensor(transition_dict['rewards'],\n",
    "                               dtype=torch.float).view(-1, 1).to(self.device)\n",
    "        next_states = torch.tensor(transition_dict['next_states'],\n",
    "                                   dtype=torch.float).to(self.device)\n",
    "        dones = torch.tensor(transition_dict['dones'],\n",
    "                             dtype=torch.float).view(-1, 1).to(self.device)\n",
    "\n",
    "        q_values = self.q_net(states).gather(1, actions)\n",
    "        if self.dqn_type == 'DoubleDQN':\n",
    "            max_action = self.q_net(next_states).max(1)[1].view(-1, 1)\n",
    "            max_next_q_values = self.target_q_net(next_states).gather(\n",
    "                1, max_action)\n",
    "        else:\n",
    "            max_next_q_values = self.target_q_net(next_states).max(1)[0].view(\n",
    "                -1, 1)\n",
    "        q_targets = rewards + self.gamma * max_next_q_values * (1 - dones)\n",
    "        dqn_loss = torch.mean(F.mse_loss(q_values, q_targets))\n",
    "        self.optimizer.zero_grad()\n",
    "        dqn_loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "        if self.count % self.target_update == 0:\n",
    "            self.target_q_net.load_state_dict(self.q_net.state_dict())\n",
    "        self.count += 1\n",
    "\n",
    "\n",
    "random.seed(0)\n",
    "np.random.seed(0)\n",
    "env.seed(0)\n",
    "torch.manual_seed(0)\n",
    "replay_buffer = rl_utils.ReplayBuffer(buffer_size)\n",
    "agent = DQN(state_dim, hidden_dim, action_dim, lr, gamma, epsilon,\n",
    "            target_update, device, 'DuelingDQN')\n",
    "return_list, max_q_value_list = train_DQN(agent, env, num_episodes,\n",
    "                                          replay_buffer, minimal_size,\n",
    "                                          batch_size)\n",
    "\n",
    "episodes_list = list(range(len(return_list)))\n",
    "mv_return = rl_utils.moving_average(return_list, 5)\n",
    "plt.plot(episodes_list, mv_return)\n",
    "plt.xlabel('Episodes')\n",
    "plt.ylabel('Returns')\n",
    "plt.title('Dueling DQN on {}'.format(env_name))\n",
    "plt.show()\n",
    "\n",
    "frames_list = list(range(len(max_q_value_list)))\n",
    "plt.plot(frames_list, max_q_value_list)\n",
    "plt.axhline(0, c='orange', ls='--')\n",
    "plt.axhline(10, c='red', ls='--')\n",
    "plt.xlabel('Frames')\n",
    "plt.ylabel('Q value')\n",
    "plt.title('Dueling DQN on {}'.format(env_name))\n",
    "plt.show()"
   ]
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